The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

AI-Assisted Coding: My Experience as a Startup Founder

wentin’s newsletter • 39 implied HN points • 31 May 23
🕹 Technology AI Programming Software Development Educational Technology
  1. AI can assist startup founders in various tasks like coding, content creation, and email management.
  2. Using AI tools like Copilot and ChatGPT can streamline code documentation and help in creating API documentation more efficiently.
  3. AI can be used to quickly generate proof-of-concept code for new features and even write an entire app from scratch, speeding up learning and development processes.

Measuring the Productivity of Developers

burkhardstubert • 39 implied HN points • 04 Oct 23
🕹 Technology Software Development Productivity Metrics Organizational Culture Team Dynamics
  1. McKinsey suggests measuring developer productivity using new metrics that track time spent on development versus other tasks. This way, they want to see more time in real coding and less in meetings.
  2. Orosz and Beck argue that measuring effort or output isn't very useful because people might manipulate those numbers. Instead, they say to focus on the actual effects of the work, like the value that reaches customers.
  3. Team performance is more important than individual effort. It's better to measure how well a team works together than to judge each person separately.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Why Kotlin is eating Java [Storytime Saturdays]

Technology Made Simple • 39 implied HN points • 09 Apr 23
🕹 Technology Programming Software Development Tech Trends Industry insights Professional development
  1. Big companies are increasingly adopting Kotlin over Java for their workflows due to its multi-functionality and great design.
  2. According to surveys, Kotlin has been consistently well-liked and is expanding beyond Android development to other platforms.
  3. Understanding why Google initially chose Java for Android, what issues Java presented, and what makes Kotlin appealing to various organizations can provide valuable insights for tech professionals.

Master The Business Benefits Behind Dependency Injection

🔮 Crafting Tech Teams • 39 implied HN points • 06 Aug 23
🕹 Technology Software Development
  1. Understanding dependency injection goes beyond configuring files and constructors; it has significant business benefits.
  2. Chaotic teams may focus on cash flow over profit, but mastering dependency injection can help streamline the business cycle and improve overall efficiency.
  3. For tech leads and managers, grasping the business impacts of dependency injection can lead to more effective decision-making and team management.

Corrective RAG (CRAG)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Feb 24
🕹 Technology AI Data Machine Learning Information Retrieval Software Development
  1. Corrective Retrieval Augmented Generation (CRAG) helps improve how data is used in language models by correcting errors from retrieved information.
  2. It uses a special tool called a retrieval evaluator to check the quality of the data and decide if it's correct, incorrect, or unclear.
  3. CRAG is designed to work well with different systems, making it easier to apply in various situations while enhancing document use.

Rethinking The Software Development Life Cycle

Beekey’s Substack • 2 HN points • 31 Jul 24
🕹 Technology Software Development Agile Methodology Project management User Experience
  1. The traditional waterfall model of software development rarely works well. Projects often go over budget, and the software can end up being unusable.
  2. Agile development was created to improve this, but many teams still stick to outdated processes and struggle with meeting user needs.
  3. Involving users early by writing code during requirements gathering can lead to better feedback and faster development, making sure the software created is valuable.

Episode 33: Better Built By Burkhard

burkhardstubert • 99 implied HN points • 01 Jan 23
🕹 Technology Software Development Testing Productivity Continuous Delivery
  1. Test-Driven Development (TDD) helps developers get quick feedback while coding, improving overall project quality. This means fewer mistakes and less time spent fixing problems later.
  2. Using TDD can reduce the complexity of code by breaking down problems into smaller parts, making it easier to manage and understand.
  3. TDD encourages a culture of continuous improvement and teamwork, allowing all developers to take responsibility for the code they write. This leads to better collaboration and a more successful project.

I made 1+1=0 in DuckDB

VuTrinh. • 19 implied HN points • 03 Feb 24
🕹 Technology Data Engineering Databases Analytics Software Development Programming
  1. DuckDB is easy to use because it works like SQLite, running directly inside applications without needing a separate server. This makes it simpler to manage.
  2. It processes data in batches through vectorization, which means it can handle multiple records at once, making operations faster than traditional row-by-row processing.
  3. DuckDB supports ACID transactions, ensuring that data remains safe and reliable, which is important in data analytics and shared environments.

Breaking Into Tech

Jake [Building in NYC] • 19 implied HN points • 03 Feb 24
🕹 Technology Software Development Career Advice Technical Skills Soft skills Job Market
  1. To get a job in software engineering, you need to learn key technical skills like React, Typescript, and some backend basics. Focus on building small projects to practice what you've learned.
  2. Having good communication, flexibility, and grit is just as important as technical skills. Being open to learning and asking questions can really help you succeed in your first job.
  3. Networking and finding a mentor can make a big difference in breaking into tech. Building relationships and getting support from experienced people is key to finding job opportunities.

The Tech Buffet #23: What Nobody Tells You About RAGs

The Tech Buffet • 1 HN point • 22 Aug 24
🕹 Technology Machine Learning Software Development Data science Artificial Intelligence Tech Reviews
  1. It's important to understand the business needs before jumping into building a Retrieval-Augmented Generation (RAG) system. Knowing the user's context and how they will use the system will save time and improve outcomes.
  2. Different types of data need to be indexed in specific ways for a RAG to work effectively. This means treating text, images, tables, and code differently to maximize the system's performance.
  3. The quality of the data chunks you use significantly affects the answers generated by a RAG. Taking the time to create clear, relevant chunks will lead to better responses from the system.

MultiHop-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 31 Jan 24
🕹 Technology AI Machine Learning Data science Software Development Language Models
  1. Multi-hop retrieval-augmented generation (RAG) helps answer complex questions by pulling information from multiple sources. It connects different pieces of data to create a clear and complete answer.
  2. Using a data-centric approach is becoming more important for improving large language models (LLMs). This means focusing on the quality and relevance of the data to enhance how models learn and generate responses.
  3. The development of prompt pipelines in RAG systems is gaining attention. These pipelines help organize the process of retrieving and combining information, making it easier for models to handle text-related tasks.

Getting Faster for Your Own LLM Inference

The Beep • 19 implied HN points • 28 Jan 24
🕹 Technology AI Computing Machine Learning Software Development Data processing
  1. Lowering the precision of LLMs can make them run faster. Switching from 32-bit to 16 or even 8-bit can save memory and boost speed during processing.
  2. Using prompt compression helps reduce the amount of information LLMs have to process. By making prompts shorter but still meaningful, the workload is lighter and speeds up performance.
  3. Quantization is a key technique for making LLMs usable on everyday computers. It allows big models to be more manageable by reducing their size without losing too much accuracy.

Tech Talks Weekly #4

Tech Talks Weekly • 19 implied HN points • 19 Feb 24
🕹 Technology Software Development Web Development Data science Cloud Computing Programming Languages
  1. The newsletter summarizes recent tech talks from various conferences, making it easier for readers to find valuable content. It's a great resource for anyone interested in technology.
  2. Each issue features a selection of must-watch talks, along with a list of new uploads categorized by conference. This helps viewers easily discover trending topics in tech.
  3. Readers are encouraged to provide feedback on the newsletter format and share it with friends or colleagues to grow the community. It's all about connecting more people to interesting tech discussions.

Visualise & Discover RAG Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 23 Jan 24
🕹 Technology AI Data science Software Development Machine Learning Data Visualization
  1. RAGxplorer is a tool that helps visualize and explore data chunks, making it easier to understand how they relate to different topics.
  2. The process of Retrieval-Augmented Generation (RAG) involves breaking documents into smaller chunks to improve how data is retrieved and used with language models.
  3. Visualizing data can help identify problems like missing information or unexpected results, allowing users to refine their questions or understand their data better.

LangSmith by LangChain

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 22 Jan 24
🕹 Technology AI Tools Software Development Data science Machine Learning Programming Languages
  1. LangSmith helps organize and manage projects and data for applications built with LangChain. It allows you to see your tasks in a neat layout and check performance easily.
  2. The platform offers tools for testing and improving agents, especially when handling multiple tasks at the same time. This helps ensure that applications run smoothly.
  3. LangSmith allows users to create datasets that can improve agent performance. It also has features to evaluate how well agents are doing by comparing their outputs to expected results.

Popular LLM Datasets

The Beep • 19 implied HN points • 21 Jan 24
🕹 Technology Machine Learning Data science Artificial Intelligence Natural Language Processing Software Development
  1. Datasets are crucial for training machine learning models, including language models. They help the model learn patterns and make predictions.
  2. Popular sources for datasets include Project Gutenberg and Common Crawl, which provide large amounts of text data for training language models.
  3. Instruction tuning datasets are used to adapt pre-trained models for specific tasks. These help the model perform better in given situations or instructions.

[Solution]Problem 65:Find the Fixed Point in a sorted array[Apple]

Technology Made Simple • 59 implied HN points • 18 Nov 22
🕹 Technology Coding Algorithms Software Development Tech Solutions
  1. A fixed point in a sorted array is an element whose value matches its index. Binary search can be used to efficiently find a fixed point if the array is sorted.
  2. When optimizing algorithms, focus on improving the major components like loop traversal to enhance the overall performance.
  3. In sorted arrays, utilizing comparators and the inherent comparison order can simplify the coding process and boost efficiency.

Data Prepare of Basic Retrieval Augmented Generation

The Beep • 19 implied HN points • 18 Jan 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Software Development Data processing
  1. Retrieval Augmented Generation (RAG) helps combine general language models with specific domain knowledge. It acts like a plugin that makes models smarter about particular topics.
  2. To prepare data for RAG, you need to load, split, and create vector stores from your documents. This process helps in organizing and retrieving relevant information efficiently.
  3. Using RAG can improve the accuracy of responses from language models. By providing context from relevant documents, you can reduce errors and make the information shared more reliable.

OpenAI isn’t Exciting Any Longer

Sector 6 | The Newsletter of AIM • 39 implied HN points • 27 Jun 23
🕹 Technology AI Machine Learning Data science Software Development Tech industry
  1. OpenAI is losing talented employees to Google, indicating a shift in the competitive landscape of AI.
  2. Some former OpenAI staff are unhappy with leadership, feeling that the company's vision is too focused on ChatGPT.
  3. There are concerns about the lack of direction at OpenAI, with rumors about the CEO's understanding of the business being superficial.

[Solution]Problem 52:Search 2D Matrix[Microsoft]

Technology Made Simple • 79 implied HN points • 18 Aug 22
🕹 Technology Coding Algorithms Software Development
  1. Understanding the problem clearly is crucial in finding efficient solutions. This particular problem doesn't require special knowledge, just logic and basic algebra.
  2. Recognizing patterns and properties of the data can significantly enhance the algorithm. In this case, the unique rules about the sorted matrix rows were key to optimizing the search process.
  3. Optimizing code by leveraging insights about the data structures can simplify solutions and reduce unnecessary complexity. It's important to make the most of the given information to write efficient algorithms.

GroupBy #18: Uber - GC Tuning for Improved Presto Reliability, How Meta is advancing GenAI

VuTrinh. • 19 implied HN points • 16 Jan 24
🕹 Technology Data Engineering Artificial Intelligence Cloud Computing Software Development System reliability
  1. Uber improved its Presto reliability by tuning garbage collection. This helps the system run better and more dependably.
  2. Meta is making strides in generative AI, focusing on how it can bring new advancements. The future looks promising for AI technologies.
  3. Python 3.13 introduced a Just-In-Time (JIT) compiler, which could speed up programming processes. This is a beneficial development for Python users.

Software Development and Fear in The Age of AI

Zakaria’s Substack • 2 HN points • 25 Jul 24
🕹 Technology Software Development AI Integration Innovation Future Trends
  1. There's a lot of fear about AI taking over jobs in software development, but these fears might be exaggerated. While AI can help speed up some tasks, it still needs engineers to solve unique problems.
  2. Large Language Models (LLMs) like GPT-4 can be helpful for mundane tasks like translating text and generating basic code, but they struggle with complex, unique challenges. Their creative solutions often don't fit specific needs.
  3. Using AI tools can make it easier for solo entrepreneurs to code, allowing them to focus on bigger decisions. Learning to work with AI is a valuable skill in today's software development world.

To hack or not to hack

Sunday Letters • 179 implied HN points • 24 Jan 22
🕹 Technology Software Development Programming Software Engineering Code Quality Technical Debt
  1. In software development, it's a challenge to choose between making a general solution or focusing on a specific problem. Both approaches have their pros and cons.
  2. If you hack your code without planning, it can become messy and hard to manage. But if you overthink it and try to make it too general too soon, you might waste time and effort.
  3. To find the right balance, ask how hard it is to change things later and how long the general solution will take to pay off. It's about making smart decisions based on the problem at hand.

My Introduction

Become a Senior Engineer • 19 implied HN points • 09 Jan 24
🕹 Technology Software Development Freelancing E-commerce
  1. The author's journey in software development started with self-teaching, from HTML to dynamic elements with JavaScript and PHP. Dreamweaver was a helpful tool for learning.
  2. After a period of exploring different jobs, the author's career accelerated when they focused on e-commerce, leading to full-time software engineering roles.
  3. The author achieved a six-figure salary milestone in a SaaS company after overcoming self-doubt. They highlight the importance of continuous learning and self-improvement, even after facing setbacks like getting fired.

GroupBy #17: Pinterest’s new wide column database using RocksDB, Fault tolerance Kafka on Kubernetes at Grab

VuTrinh. • 19 implied HN points • 09 Jan 24
🕹 Technology Data Engineering Software Development Database Systems Cloud Computing Big Data
  1. Pinterest has developed a new wide column database using RocksDB for better data handling. This helps them manage large amounts of data more efficiently.
  2. Grab improved Kafka's fault tolerance on Kubernetes, ensuring their real-time data streaming service runs smoothly even when problems occur.
  3. The newsletter will evolve, offering more content types like curated resources on data engineering and personal insights every week.

Lets compile Linux DOOM

Deus In Machina • 36 implied HN points • 01 Feb 24
🕹 Technology Game Development Software Development Open Source Programming Languages Operating Systems
  1. Compiling the Linux DOOM source code requires setting up the source code from the id-software repository and navigating through different build methods like Make and CMake.
  2. Encountering and solving errors in the compilation process involves making adjustments to data types, structure pointers, and handling variables like errno to ensure successful building of the DOOM executable.
  3. To address color depth issues and display errors while running the DOOM game on modern systems, utilizing tools like Xephyr, setting specific environmental variables, and modifying code sections related to color maps and display resolutions becomes critical.

Super Weights in LLMs - How Pruning Them Destroys a LLM's Ability to Generate Text ?

Machine Learning Diaries • 3 implied HN points • 18 Nov 24
🕹 Technology Machine Learning Artificial Intelligence Data science Model optimization Software Development
  1. Super weights are very important for how well large language models (LLMs) perform. Even though they're a tiny part of the model, they can greatly affect the results.
  2. If a super weight is removed, it can ruin the model's ability to generate clear text and make predictions. Just taking out one of these weights can cause a huge drop in performance.
  3. Removing regular outlier weights doesn't harm performance much, but losing just one super weight is much worse than taking out a lot of other weights combined.

Mastering the Craft: Why Reading Other People's Code Makes You Code Like A Pro

Weekend Developer • 39 implied HN points • 19 May 23
🕹 Technology Coding Software Development Collaboration Learning
  1. Reading code written by experienced programmers exposes you to different techniques and approaches, enhancing your problem-solving capabilities.
  2. By studying established best practices in code, like proper organization and naming conventions, you develop good coding habits from the start, resulting in more maintainable and readable code.
  3. Understanding complex systems through reading code helps you build crucial skills for professional software development, such as the ability to work with large codebases and collaborate effectively with other developers.

Any Cables Monthly #17

AnyCable Broadcasts • 19 implied HN points • 02 Jan 24
🕹 Technology Programming Web Development Software Development Innovations
  1. Exciting real-time development initiatives are coming in 2024, like AnyCable+ and Laravel Reverb.
  2. Action Cable server adapterization project aims to improve Rails Action Cable for easier use with different server implementations.
  3. Potential real-time component upgrade in Laravel with 'Laravel Reverb' and mysterious project 'SolidCable' from DHH.

GroupBy #16: Uber's Anomaly Detection & Alerting System, many layers of data lineage

VuTrinh. • 19 implied HN points • 02 Jan 24
🕹 Technology Data Engineering Machine Learning Software Development Data Analytics Cloud Computing
  1. Uber has developed an anomaly detection system called uVitals, which helps identify issues before they become major problems. It analyzes data patterns to catch anomalies early.
  2. Data modeling is essential for creating structured databases that allow for better analysis and comparisons. It's important for data projects to have clear designs.
  3. As the field of data engineering evolves, new roadmaps and resources are emerging to guide professionals in developing necessary skills. Staying updated can help engineers advance their careers.

Building a Compliance and AppSec Program for a Federal Platform-as-a-Service (PaaS)

Resilient Cyber • 59 implied HN points • 11 Apr 23
🕹 Technology Cybersecurity Cloud Computing DevOps Compliance Software Development
  1. Building a compliance and AppSec program for a federal Platform-as-a-Service is challenging. It's important to understand which security controls can be inherited by development teams.
  2. Scaling the compliance program across multiple teams can lead to unique challenges. It's crucial to onboard each team effectively while minimizing their workload.
  3. Developers need support in balancing security and compliance with their work. Educating auditors about cloud practices is also important for smoother collaboration.